Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums

Ronnie S. Concepcion II, Elmer P. Dadios


Ecophysiological stimulators directly affect root morphology, especially in the embryonic stage. To enhance crop germination, an understanding of the root traits under abiotic inducers is needed. In this study, the combined impacts of white and red-blue light spectrums, cold stratification, and seed fortification involving various concentrations of bioactive chemicals namely simple nutrient addition program solution, gibberellic acid, α-naphthaleneacetic acid with thiamine hydrochloride were evaluated on loose-leaf lettuce (Lactuca sativa var. Altima) seedling root architecture. The growth-promoting effects of these nutrients varied the growth rate and morphology of roots which are immediately shown during the radicle development. Integrated computer vision and computational intelligence were employed for phytomorphological signatures extraction of seedlings that were cultivated in a customized modulable spectrum experimental chamber (MSPEC). Root phenotype model was developed using graph-cut segmentation and region properties, and the ideal germination nutrient concentration was optimized using bioinspired models with firefly algorithm optimal result of 204.1 mg/L for nitrate, 238.15 mg/L for phosphate, and 158.08 mg/L for potassium. It was verified that lettuce seedlings can endure highly concentrated nutrients, however, it is more sensitive to phosphate as this macronutrient significantly promotes root growth with the increased whorl number on white light spectrum exposure with cold stratification.


Bioinspired algorithm; Computer vision; Lettuce; Root architecture phenotyping; Seed fortification and stratification

Full Text:



Alaguero-Cordovilla, A., Gran-Gómez, F. J., TormosMoltó, S., & Pérez-Pérez, J. M. (2018). Morphological characterization of root system architecture in diverse tomato genotypes during early growth. International Journal of Molecular Sciences, 19(12), 3888. crossref

Alejandrino, J., Concepcion, R., Lauguico, S., Tobias, R. R., Almero, V. J., Puno, J. C., ... Flores, R. (2020). Visual classification of lettuce growth stage based on morphological attributes using unsupervised machine learning models. In 2020 IEEE Region 10 Conference (TENCON) (pp. 438-443). Osaka, Japan: IEEE. crossref

Clark, R. T., MacCurdy, R. B., Jung, J. K., Shaff, J. E., McCouch, S. R., Aneshansley, D. J., & Kochian, L. V. (2011). Three-dimensional root phenotyping with a novel imaging and software platform. Plant Physiology, 156(2), 455–465. crossref

Concepcion, R. S., Lauguico, S. C., Alejandrino, J. D., Dadios, E. P., & Sybingco, E. (2020). Lettuce canopy area measurement using static supervised neural networks based on numerical image textural feature analysis of haralick and gray level co-occurrence matrixs. AGRIVITA Journal of Agricultural Science, 42(3), 472–486. crossref

Concepcion, R. S., Lauguico, S., Tobias, R. R., Dadios, E., Bandala, A., & Sybingco, E. (2020). Genetic algorithm-based visible band tetrahedron greenness index modeling for lettuce biophysical signature estimation. In IEEE Region 10 Conference (TENCON) (pp. 679–684). Osaka, Japan: IEEE. crossref

Falk, K. G., Jubery, T. Z., Mirnezami, S. V., Parmley, K. A., Sarkar, S., Singh, A., … Singh, A. K. (2020). Computer vision and machine learning enabled soybean root phenotyping pipeline. Plant Methods, 16, 5. crossref

Fu, Y., Li, H. Y., Yu, J., Liu, H., Cao, Z. Y., Manukovsky, N. S., & Liu, H. (2017). Interaction effects of light intensity and nitrogen concentration on growth, photosynthetic characteristics and quality of lettuce (Lactuca sativa L. Var. youmaicai). Scientia Horticulturae, 214, 51–57. crossref

Ghorchiani, M., Etesami, H., & Alikhani, H. A. (2018). Improvement of growth and yield of maize under water stress by co-inoculating an arbuscular mycorrhizal fungus and a plant growth promoting rhizobacterium together with phosphate fertilizers. Agriculture, Ecosystems and Environment, 258, 59–70. crossref

Iglesias, M. J., Colman, S. L., Terrile, M. C., París, R., Martín-Saldaña, S., Chevalier, A. A., … Casalongué, C. A. (2019). Enhanced properties of chitosan microparticles over bulk chitosan on the modulation of the auxin signaling pathway with beneficial impacts on root architecture in plants. Journal of Agricultural and Food Chemistry, 67(25), 6911–6920. crossref

Izzo, L. G., Hay Mele, B., Vitale, L., Vitale, E., & Arena, C. (2020). The role of monochromatic red and blue light in tomato early photomorphogenesis and photosynthetic traits. Environmental and Experimental Botany, 179, 104195. crossref

Kerbiriou, P. J., Stomph, T. J., Van Der Putten, P. E. L., Lammerts Van Bueren, E. T., & Struik, P. C. (2013). Shoot growth, root growth and resource capture under limiting water and N supply for two cultivars of lettuce (Lactuca sativa L.). Plant and Soil, 371, 281–297. crossref

Lauguico, S. C., Concepcion, R. I. S., Alejandrino, J. D., Tobias, R. R., & Dadios, E. P. (2020). Lettuce life stage classification from texture attributes using machine learning estimators and feature selection processes. International Journal of Advances in Intelligent Informatics, 6(2), 173–184. crossref

Margenot, A. J., Rippner, D. A., Dumlao, M. R., Nezami, S., Green, P. G., Parikh, S. J., & McElrone, A. J. (2018). Copper oxide nanoparticle effects on root growth and hydraulic conductivity of two vegetable crops. Plant and Soil, 431, 333–345. crossref

Miguel, M. A., Widrig, A., Vieira, R. F., Brown, K. M., & Lynch, J. P. (2013). Basal root whorl number: A modulator of phosphorus acquisition in common bean (Phaseolus vulgaris). Annals of Botany, 112(6), 973–982. crossref

Moore, C. R., Johnson, L. S., Kwak, I. Y., Livny, M., Broman, K. W., & Spalding, E. P. (2013). Highthroughput computer vision introduces the time axis to a quantitative trait map of a plant growth response. Genetics, 195(3), 1077–1086. crossref

Moreira, I. N., Martins, L. L., & Mourato, M. P. (2020). Effect of Cd, Cr, Cu, Mn, Ni, Pb and Zn on seed germination and seedling growth of two lettuce cultivars (Lactuca sativa L.). Plant Physiology Reports, 25, 347–358. crossref

Puno, J. C., Sybingco, E., Dadios, E., Valenzuela, I., & Cuello, J. (2017). Determination of soil nutrients and pH level using image processing and artificial neural network. In IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1–6). Manila, Philippines: IEEE. crossref

Rangarajan, H., Postma, J. A., & Lynch, J. P. (2018). Cooptimization of axial root phenotypes for nitrogen and phosphorus acquisition in common bean. Annals of Botany, 122(3), 485–499. crossref

Rufo, R., Salvi, S., Royo, C., & Soriano, J. M. (2020). Exploring the genetic architecture of rootrelated traits in Mediterranean bread wheat landraces by genome-wide association analysis. Agronomy, 10(5), 613. crossref

Shi, L., Shi, T., Broadley, M. R., White, P. J., Long, Y., Meng, J., … Hammond, J. P. (2013). Highthroughput root phenotyping screens identify genetic loci associated with root architectural traits in Brassica napus under contrasting phosphate availabilities. Annals of Botany, 112(2), 381–389. crossref

Solano, C. J., Hernández, J. A., Suardíaz, J., & BarbaEspín, G. (2020). Impacts of leds in the red spectrum on the germination, early seedling growth and antioxidant metabolism of pea (Pisum sativum L.) and melon (Cucumis melo L.). Agriculture, 10(6), 204. crossref

Strock, C. F., Burridge, J., Massas, A. S. F., Beaver, J., Beebe, S., Camilo, S. A., … Lynch, J. P. (2019). Seedling root architecture and its relationship with seed yield across diverse environments in Phaseolus vulgaris. Field Crops Research, 237, 53–64. crossref

Teramoto, S., Takayasu, S., Kitomi, Y., Arai-Sanoh, Y., Tanabata, T., & Uga, Y. (2020). High-throughput three-dimensional visualization of root system architecture of rice using X-ray computed tomography. Plant Methods, 16, 66. crossref

Tomar, R. S. S., Tiwari, S., Vinod, Naik, B. K., Chand, S., Deshmukh, R., … Tomar, S. M. S. (2016). Molecular and morpho-agronomical characterization of root architecture at seedling and reproductive stages for drought tolerance in wheat. PLoS ONE, 11(6), e0156528. crossref

Wang, B., & Shen, Q. (2012). Effects of ammonium on the root architecture and nitrate uptake kinetics of two typical lettuce genotypes grown in hydroponic systems. Journal of Plant Nutrition, 35(10), 1497–1508. crossref

Wang, W., Liu, J., Ren, Y., Zhang, L., Xue, Y., Zhang, L., & He, J. (2020). Phytotoxicity assessment of copper oxide nanoparticles on the germination, early seedling growth, and physiological responses in Oryza sativa L. Bulletin of Environmental Contamination and Toxicology, 104, 770–777. crossref

Wei, Z., Julkowska, M. M., Laloë, J. O., Hartman, Y., de Boer, G. J., Michelmore, R. W., … Schranz, M. E. (2014). A mixed-model QTL analysis for salt tolerance in seedlings of crop-wild hybrids of lettuce. Molecular Breeding, 34, 1389–1400. crossref

Wu, H., Asaduzzaman, M., Shephard, A., Hopwood, M., & Ma, X. (2020). Germination and emergence characteristics of prickly lettuce (Lactuca serriola L.). Crop Protection, 136, 105222. crossref

Yasrab, R., Atkinson, J. A., Wells, D. M., French, A. P., Pridmore, T. P., & Pound, M. P. (2019). RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures. GigaScience, 8(11), giz123. crossref


Copyright (c) 2021 The Author(s)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.